Data mining techniques have a veritable role in business development for start-up businesses especially. By exploring the best data mining techniques, companies can gain insights from the untapped potential of their data. Information is a priceless asset for companies and the right strategies can turn raw information into marketing tools.
Data mining is the process of extracting meaningful and usable data from the voluminous and scattered raw data. There are various data mining techniques that offer a lot to start up businesses. Data mining can be employed in various business processes including customer relations, sales and marketing and more. Data mining techniques will also enable startups to explore various factors like the assessment of risks and opportunities and resource allocations. Which in turn will make them competent enough to face challenges and to make well-informed decisions.
Types of Data Mining Techniques
There are four primary types of data mining techniques that the entire industry uses:
1. No-Coupling Data Mining
It is a very simple data mining process and doesn’t rely on information offered by the database or data warehouse system, as it retrieves data from a specific source before processing through different algorithms and storing the processed results in a file system. It is not recommended for businesses that use a database or data warehouse system.
The main advantage of this technique is that it uses different data mining algorithms and store results in the file system. The disadvantage is that it doesn’t utilize the existing database even if it is effective in organizing and storing data.
2. Loose Coupling Data Mining
In this system, the data mining system retrieves data from within a database and stores results. It makes use of some functions of DB and DW systems to gather data and store the results in a file, data warehouse, or database.
The advantage of this technique is real-time data and low latency and affordability considering the fact data localization aren’t necessary. The disadvantage of this data mining method is possible issues with semantics within the schema and a slower query response because of unlocalized data. As this data mining technique is likely to use sources like flat files to gather initial data sets for mining, it is often considered as a weak design scheme.
3. Semi-Tight Coupling Data Mining
This system makes use of various data lakes or data warehouse system components for data mining activities and results are stored in a database or data warehouse.
Advantages of this system include the ease to link it to a database or warehouse system, to utilize different features of the data warehouse.
4. Tight Coupling Data Mining
In this technique, the database is used to retrieve information for data mining. This technique ensures scalability, better performance, and data integrity.
The main advantage of this technique is a lower dependency on source systems because the data is copied over. It ensures speedy and complex query processing and quick processing of even vol